Other line of traditional methods [1]}, [2]} tried to embed the source and target data into a Grassmann manifold and learn a specific geodesic path between the two domains to bridge the source and target domains, but they are not easily applicable to deep model. Following them, [3]} proposed a person re-identification method based on the shortest geodesic path definition, forcing the positioning of the intermediate domain on the appropriate path between the source and target domains in the manifold, but their method is designed for supervised domain adaptation since their bridging loss involves labels from the target-train set. [4]} proposed a GAN-based source domain-like target domain image, but it is computationally expensive and GAN-based methods may cause mode collapse. [5]} proposed a transferable semantic augmentation approach to enhance the classifier adaptation ability, but missed the feature admixture association between different categories.
